🌍 The real elephant in the room in the LLM debate is their astonishing resource consumption.

Did you know training one model can equate to 284 years of carbon emissions from an average car? As we push for larger models, the challenge of deploying these efficiently becomes crucial. Enter Low-Rank Adaptation (LoRA): a cutting-edge approach that dramatically reduces the need for extensive retraining by freezing pre-trained model weights and inserting trainable rank decomposition matrices at each layer. This technique cuts down the trainable parameters by up to 10,000 times and the GPU memory requirement by threefold, without sacrificing performance or adding inference latency. 🚀 Moreover, embracing serverless architectures can further reduce our carbon footprint. By optimizing resource allocation dynamically, serverless computing ensures that we only use the power we need, when we need it—perfect for deploying AI models more sustainably. Interested in learning more about how you can deploy LLMs cheaply being mindful of environmental concerns? Let me know your thoughts below.